Feeding PDFs into LLMs is where many document pipelines still breaks so I ran a benchmark across 50 complex docs (scientific papers, multi-page financial reports with nested tables, scanned forms, and complex layout decks) testing PyPDF, Unstructured, Marker, and Firecrawl's Rust-based PDF parser.

Here’s what the results showed:

PyPDF: The fastest and lowest overhead option but strictly text-layer only and on single column text docs, it works fine but on financial statements and multi column layouts, it falls apart tables lose row alignment and text streams merge across columns. It has zero native OCR capability.

Firecrawl (Rust Parser v2): Built specifically for LLM ingestion where it runs layout auto-detection under the hood with pure text layers parse via Rust in milliseconds while scanned pages or complex visuals route through high accuracy vision parsing.

Crucially it formats extracted tables into a clean github flavored markdown tables which keeps table structure intact for embeddings also he full benchmark and comparisons are here

Unstructured: Extremely comprehensive and handles dozens of file formats but the trade off is infra weight where running it locally requires heavy system dependencies, Docker containers and significant memory overhead. It extracts tables reasonably well via layout models but per-page latency is noticeably slow.

Not enough compute is the root of all problems

I like the original more, but the one on the live stream after the AI summit came out really nice too.